The Ultimate Guide to Link Analysis Software

What is Link Analysis Software?

Link analysis is a data analysis technique used to evaluate relationships between entities within large datasets. In this context, entities (or nodes) can represent people, organizations, events, and more, while links (or edges) signify the relationships between them. The visual representation of these connections is crucial for identifying patterns and uncovering hidden relationships. However, link analysis software offers much more than just data visualization. It provides powerful traversal capabilities, enabling users to explore and investigate connections between data points. This functionality is essential for finding clues or confirming investigative hypotheses.

How Link Analysis Software Works?

Link analysis software represents data as networks of nodes and edges, enabling the discovery of hidden connections through network analysis. There are two main architectural approaches to link analysis software.

The traditional approach involves reconstructing graphs from relational tables at the application level, which can be inefficient and complex. The modern approach stores data natively in graph structures, such as graph databases or knowledge graphs, allowing analysts to query relationships directly, without the need to rebuild the graph for every query.

These approaches illustrate the evolution of data analysis in crime and intelligence work. While early tools focused on linking specific data points manually on an investigation canvas, modern systems aim to integrate organisation-wide data to uncover deeper patterns and previously unseen connections.

The traditional model is increasingly unsuitable in today’s data landscape, where zettabytes of information are generated daily. This growing complexity has driven the shift towards graph architectures, which are purpose-built to handle interconnected data at scale.

Graph-Powered Link Analysis

Traditional link analysis relies on table-based approaches, which often encounter limitations such as computational power constraints and difficulties in visualizing connections beyond two hops. In contrast, graph solutions overcome these challenges by persisting data relations, allowing for unlimited hop connections, flexible schemas, and seamless integration of additional data sources.

Computational Complexity

In real world scenarios, link analysis involve gigabytes of data, and traversing just a few ownership layers by performing table joins across large datasets can quickly overwhelm system memory and processing capacity. GraphAware link analysis software architecture use graph queries that can follow all links in a network in a single operation, regardless of the number of steps. Performance does not degrade as the network grows deeper. 

Lack of Persisted Relationships

Analysts are often required to answer time related questions, such as adding temporal dimension to the query. This requires the inclusion of temporal data to track when relationship started and ended. Because relational databases do not persist relationships as part of their structure, analysts must start from scratch each time new question arises: running new queries and rebuilding the ownership chain in full. 

GraphAware link analysis software on the other hand both nodes and relationships in the database can include specific properties, such as start and end dates. Adding temporal dimension to the investigative question requires only a single additional parameter in the query.

GraphAware analysis allows for flexible exploration. New data, such as transaction records or regulatory filings, can be added to the existing graph without disrupting the structure. This makes it easier to detect new patterns and connections as they emerge.

Traditional vs. Graph-Powered Link Analysis Comparison

Data Ingestion

Effective link analysis begins with connecting data from various sources. Tools like GraphAware Hume facilitate data ingestion by integrating fragmented data into a unified view, enabling comprehensive analysis.

Data Visualization

Visualization is critical for identifying patterns and connections quickly. GraphAware’s Hume offers a native visualization library optimized for graph databases, providing fast and interactive interfaces that include geo and temporal views.

Data Integration and Enrichment

Link analysis software often connects to diverse data sources, both structured and unstructured. Features like data normalization and entity resolution ensure consistency, while enrichment with external intelligence sources provides a single, accurate view of the data.

Analytical Capabilities

Advanced analytical features such as multihop connections, shortest path algorithms, and community detection enable analysts to uncover complex patterns and key relationships within criminal networks.

Reporting and Collaboration

Effective collaboration is supported through features that allow saving, sharing, and retrieving link charts. Automated alerting and customizable reports and dashboards enhance the ability to communicate insights and monitor patterns of interest.

Link analysis software is an indispensable asset for crime analysts, enhancing investigative efficiency, pattern identification, and predictive capabilities. GraphAware’s connected data analytics solutions offer a robust, graph-native approach that addresses the limitations of traditional tools. By leveraging GraphAware, crime analysts can unlock deeper insights and drive more effective law enforcement strategies.